design and development of an early optical disease recognition system using fundus imaging on fpga

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DESIGN AND DEVELOPMENT OF AN EARLY OPTICAL DISEASE RECOGNITION SYSTEM USING FUNDUS IMAGING ON FPGA

2 NEED FOR THE PROJECT

Individuals with untreated polygenic disorder are twenty five times more in danger for vision defect than the final population.

The longer an individual has had polygenic disorder, the higher the chance of developing diabetic retinopathy.

But with regular, correct eye care and treatment at the proper time, the incidence of severe vision loss are often greatly reduced.

3 PROBLEM STATEMENT

The fundus image of the healthy eye has only the optic disk in it. Whereas the fundus image of an infected eye has optic disk along with spots with the same intensity level as that of the optic disk.

These spots are called as exudates OR cotton wool spots and are characteristic of diabetic retinopathy.

We aim to extract the characteristics (exudates) obtained from the fundus image of a person’s eye.

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Fig.1 Healthy fundus image Fig.2 Infected fundus image

5 System level block diagram

Image Acquisition

Color Space Conversion

Segmentation of Optic

DiscMasking of Optic Disc

Extraction of exudates

Area calculation CBIR

6 IMAGE ACQUISITION

Fig 3. Fundus image of infected eye

7 COLOR SPACE CONVERSION

H S V

where

8 COLOR SPACE CONVERSION

Y Cb Cr

Y= 0.299R + 0.587G + 0.114B

Cb= B - Y

Cr= R - Y

9 SEGMENTATION OF OPTIC DISC

Fig. 6 Thresholding operation on fundus image using a single color component(S)

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Fig.7 Segmented optic disc before erosion and dilation

SEGMENTATION OF OPTIC DISC

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Fig 8. Segmented optic disc after erosion and dilation

SEGMENTATION OF OPTIC DISC

12 MASKING

Fig 9. RGB Image Fig 10. Segmented optic disc

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Fig 11. Image obtained after masking

MASKING

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Fig 12. RED Fig 13. GREEN

Fig 14. BLUE

EXTRACTION OF COLOR COMPONENTS FROM MASKED IMAGE

15Fig 15. EXUDATES EXTRACTED FROM MASKED IMAGE

EXUDATES EXTRACTION

16 IMPLEMENTATION IN SIMULINK

Fig.16 Area of exudates calculated for a single image implemented in Simulink

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Fig 17. Output obtained from Simulink

18 CONTENT BASED IMAGE RETRIEVAL

Content-based image retrieval (CBIR) is the application of computer vision techniques to the image retrieval problem, that is, the problem of searching for digital images in large databases.

“Content-based" means that the search analyses the contents of the image rather than the metadata such as keywords, tags, or descriptions associated with the image.

CBIR is desirable because searches that rely purely on metadata are dependent on annotation quality and completeness.


Fig. 18 A test image with a database image


Fig.19 Multiport switch and JTAG programming along with MATLAB function block for comparison

21 FUTURE WORKS

Better segmentation of optic disc can be achieved. Along with the area, the medicine to be prescribed can also be

displayed. Handheld ophthalmoscopes which can take the fundus image

without dilation of the pupil.

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Thank you!- VRUSHAK K(1BG11TE062) VIKRAM(1BG11TE061) DINESH N SHENOY(1BG11TE015)

design and development of an early optical disease recognition system using fundus imaging on fpga

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